Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States

Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making

This article reports the outcome of a project to develop and assess a predictive model of vulnerability indicators for COVID-19 infection in Los Angeles County. Multiple data sources were used to construct four indicators for zip code tabulation areas: (1) pre-existing health condition, (2) barriers to accessing health care, (3) built environment risk, and (4) the CDC's social vulnerability. The assessment of the indicators finds that the most vulnerable neighborhoods are characterized by significant clustering of racial minorities. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest levels of pre-existing health conditions. For the barriers to accessing health care indicator, 40% of Latinx reside in the highest vulnerability places. The built environment indicator finds that selected Asian ethnic groups (63%), Latinx (55%), and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability. The social vulnerability indicator finds 42% of Blacks and Latinx and 38% of selected Asian ethnic group residing in neighborhoods of high vulnerability. The vulnerability indicators can be adopted nationally to respond to COVID-19. The metrics can be utilized in data-driven decision making of re-openings or resource distribution such as testing, vaccine distribution and other pandemic-related resources to ensure equity for the most vulnerable.

Association of County-Level Social Vulnerability with Elective Versus Non-elective Colorectal Surgery

INTRODUCTION

A person's community, or lived environment, may play an important role in achieving optimal health outcomes. The objective of the current study was to assess the association of county-level vulnerability with the probability of having a non-elective colon resection. We hypothesized that individuals from areas with a high social vulnerability would be at greater risk of non-elective colon resection compared with patients from low social vulnerability areas.

METHODS

Patients aged 65-99 who underwent a colon resection for a primary diagnosis of either diverticulitis (n = 11,812) or colon cancer (n = 33,312) were identified in Medicare Part A and Part B for years 2016-2017. Logistic regression analysis was used to evaluate differences in probability of undergoing an elective versus non-elective operation from counties relative to county-level social vulnerability index (SVI). Secondary outcomes included postoperative complications, mortality, readmission, and index hospitalization expenditure.

RESULTS

Among 45,124 patients, 11,812 (26.2%) underwent a colon resection for diverticulitis, while 33,312 (73.8%) had a resection for colon cancer; 31,012 (68.7%) patients had an elective procedure (diverticulitis n = 7291 (61.7%) vs. cancer n = 23,721 (71.2%)), while 14,112 (31.3%) had an emergent operation (diverticulitis n = 4521 (38.3%) vs. cancer n = 9591 (28.8%)). Patients with a high SVI were more likely to undergo an emergent colon operation compared with low SVI patients (43.7% vs. 40.4%) (p < 0.001). The association of high SVI with increased risk of an emergent colon operation was similar among patients with diverticulitis (emergent: low SVI 37.2% vs. high SVI 40.4%) or colon cancer (emergent: low SVI 26.0% vs. high SVI 29.9%) (both p < 0.05). On multivariable analyses, risk-adjusted probability of undergoing an urgent/emergent operation remained associated with SVI (p < 0.05).

CONCLUSION

Patients residing in vulnerable communities characterized by a high SVI were more likely to undergo a non-elective colon resection for either diverticulitis or colon cancer. Patients from high SVI areas had a higher risk of postoperative complications, as well as index hospitalization expenditures; however, there were no differences in mortality or readmission rates.

Neighborhoods matter; but for whom? Heterogeneity of neighborhood disadvantage on child obesity by sex

Although evidence suggests that neighborhood context, particularly socioeconomic context, influences child obesity, little is known about how these neighborhood factors may be heterogeneous rather than monolithic. Using a novel dataset comprised of the electronic medical records for over 250,000 children aged 2-17 nested within 992 neighborhoods in the greater Houston area, we assessed whether neighborhoods influenced the obesity of children differently based on sex. Results indicated that neighborhood disadvantage, assessed using a comprehensive, multidimensional, latent profile analysis-generated measure, had a strong, positive association with the odds of obesity for both boys and girls. Interactions revealed that the relationship between disadvantage and obesity was stronger for girls, relative to boys. Our findings demonstrated the complex dynamics underlying the influence of residential neighborhood context on obesity for specific subgroups of children.

Predictive Modeling of COVID-19 Case Growth Highlights Evolving Demographic Risk Factors in Tennessee and Georgia

The COVID-19 pandemic has exposed the need to understand the unique risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections in the future. Our work combined publicly available COVID-19 statistics with county-level social determinants of health information. Machine learning models were trained to predict COVID-19 case growth and understand the unique social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. The predictive models achieved a mean r-squared (R2) of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the social determinants of health, with a specific focus on demographics, that were strongly associated with COVID-19 case growth in Tennessee and Georgia counties. The demographic results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. Identifying the specific risk factors tied to COVID-19 case growth can assist public health officials and policymakers target regional interventions to mitigate the burden of future outbreaks and minimize long-term consequences including emergence or exacerbation of chronic diseases that are a direct consequence of infection.

The multidimensional clustering of health and its ecological risk factors

A diverse set of research has examined the ways in which population-level health and its ecological risk factors are embedded within self-reinforcing structures. Syndemic theory, for example, focuses on the co-occurrence of multiple diseases, whereas the spatial diffusion literature highlights the concentration of poor health among communities sharing geographic boundaries. This study combines these related but disciplinarily-isolated perspectives to examine the clustering of population-level health and its determinants across four dimensions: co-occurrence, spatial, temporal, and social network. Using data on U.S. county-level health outcomes and health factors from the Robert Wood Johnson Foundation's County Health Rankings, this study estimates associations between health outcomes within communities and the co-occurrence of community-level factors theorized to influence ecological health. Not only do health outcomes and their ecological risk factors cluster within counties, but also between geographically adjacent counties and counties connected via migration network pathways. Moreover, the self-reinforcing structures uncovered across the co-occurrence, spatial and network dimensions persist over time, and this clustering has consequences on county health and well-being. Rather than adopting the perspective that either health and its community-level factors should be broadly targeted and detached from local context or communities are different, have unique needs and thus should be treated in isolation, the approach advanced in this study identifies shared vulnerabilities in a way that allows for the development of knowledge networks between communities dealing with similar issues.

Individual and Community Social Determinants of Health Associated With Diabetes Management in a Mexican American Population

Background: Diabetes is a major health burden in Mexican American populations, especially among those in the Lower Rio Grande Valley (LRGV) in the border region of Texas. Understanding the roles that social determinants of health (SDOH) play in diabetes management programs, both at the individual and community level, may inform future intervention strategies.

Methods: This study performed a secondary data analysis on 1,568 individuals who participated in Salud y Vida (SyV), a local diabetes and chronic disease management program, between October 2013 and September 2018 recruited from a local clinic. The primary outcome was the reduction of hemoglobin A1C (HbA1C) at the last follow-up visit compared to the baseline. In addition to age, gender, insurance status, education level and marital status, we also investigated 15 community (census tract) SDOH using the American Community Survey. Because of the high correlation in the community SDOH, we developed the community-level indices representing different domains. Using Bayesian multilevel spatial models that account for the geographic dependency, we were able to simultaneously investigate the individual- and community-level SDOH that may impact HbA1C reduction.

Results: After accounting for the diabetes self-management education classes taken by the participants and their length of stay in the program, we found that older age at baseline, being married (compared to being widowed or divorced) and English speaking (compared to Spanish) were significantly associated with greater HbA1C reduction. Moreover, we found that the community level SDOH were also highly associated with HbA1C reduction. With every percentile rank decrease in the socioeconomic advantage index, we estimated an additional 0.018% reduction in HbA1C [95% CI (−0.028, −0.007)]. Besides the socioeconomic advantage index, urban core opportunity and immigrant's cohesion and accessibility indices were also statistically associated with HbA1C reduction.

Conclusion: To our knowledge, our study is the first to utilize Bayesian multilevel spatial models and simultaneously investigate both individual- and community-level SDOH in the context of diabetes management. Our findings suggest that community SDOH play an important role in diabetes control and management, and the need to consider community and neighborhood context in future interventions programs to maximize their overall effectiveness.

Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health

Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011–2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014–2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showing larger numbers of people who are black, single moms, and unemployed along with fewer people who are white) and Affluence (with loadings showing higher income, education, and home value) were strongly associated with poor mental health (R2=0.67, R2=0.83). These findings demonstrate the utility of this factor model for future research focused on the relationship between neighborhood characteristics and resident mental health.

Social determinants of health and hospital readmissions: can the HOSPITAL risk score be improved by the inclusion of social factors?

Background

The HOSPITAL Risk Score (HRS) predicts 30-day hospital readmissions and is internationally validated. Social determinants of health (SDOH) such as low socioeconomic status (SES) affect health outcomes and have been postulated to affect readmission rates. We hypothesized that adding SDOH to the HRS could improve its predictive accuracy.

Methods

Records of 37,105 inpatient admissions at the University of Chicago Medical Center were reviewed. HRS was calculated for each patient. Census tract-level SDOH then were combined with the HRS and the performance of the resultant “Social HRS” was compared against the HRS. Patients then were assigned to 1 of 7 typologies defined by their SDOH and a balanced dataset of 14,235 admissions was sampled from the larger dataset to avoid over-representation by any 1 sociodemographic group. Principal component analysis and multivariable linear regression then were performed to determine the effect of SDOH on the HRS.

Results

The c-statistic for the HRS predicting 30-day readmission was 0.74, consistent with published values. However, the addition of SDOH to the HRS did not improve the c-statistic (0.71). Patients with unfavorable SDOH (no high-school, limited English, crowded housing, disabilities, and age > 65 yrs) had significantly higher HRS (p < 0.05 for all). Overall, SDOH explained 0.2% of the HRS.

Conclusion

At an urban tertiary care center, the addition of census tract-level SDOH to the HRS did not improve its predictive power. Rather, the effects of SDOH are already reflected in the HRS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-020-05989-7.

County-Level Social Vulnerability is Associated with Worse Surgical Outcomes Especially Among Minority Patients

OBJECTIVE

We sought to characterize the association between patient county-level vulnerability with postoperative outcomes.

SUMMARY BACKGROUND DATA

While the impact of demographic-, clinical- and hospital-level factors on outcomes following surgery have been examined, little is known about the effect of a patient's community of residence on surgical outcomes.

METHODS

Individuals who underwent colon resection, coronary artery bypass graft (CABG), lung resection, or lower extremity joint replacement (LEJR) were identified in the 2016-2017 Medicare database, which was merged with CDC vulnerability index (SVI) dataset at the beneficiary level of residence. Logistic regression models were utilized to estimate the probability of postoperative complications, mortality, readmission, and expenditures.

RESULTS

Among 299,583 Medicare beneficiary beneficiaries who underwent a colectomy (n = 88,778, 29.6%), CABG (n = 109,564, 36.6%), lung resection (n = 30,401, 10.1%), or LEJR (n = 70,840, 23.6%).Mean SVI score was 50.2 (SD: (25.2); minority patients were more likely to reside in highly vulnerable communities (low SVI: n = 3,531, 5.8% vs. high SVI: n = 7,895, 13.3%; p < 0.001). After controlling for competing risk factors, the risk-adjusted probability of a serious complication among patients from a high versus low SVI county was 10-20% higher following colectomy (OR 1.1 95%CI 1.1-1.2) or CABG (OR 1.2 95%CI 1.1-1.3), yet there no association of SVI with risk of serious complications following lung resection (OR 1.2 95%CI 1.0-1.3) or LEJR (OR 1.0 95%CI 0.93-1.2). The risk-adjusted probability of 30-day mortality was incrementally higher among patients from high SVI counties following colectomy (OR 1.1 95%CI 1.1-1.3), CABG (OR 1.4, 95%CI 1.2-1.5), and lung resection (OR 1.4 (95%CI 1.1-1.8), yet not LEJR (OR 0.95 95%CI 0.72-1.2). Black/minority patients undergoing a colectomy, CABG, or lung resection who lived in highly socially vulnerable counties had an estimate 28-68% increased odds of a serious complication and a 58-60% increased odds of 30-day mortality compared with a black/minority patient from a low socially vulnerable county, as well as a markedly higher risk than white patients (all p > 0.05).

CONCLUSION

Patients residing in vulnerable communities characterized by a high SVI generally had worse postoperative outcomes. The impact of social vulnerability was most pronounced among black/minority patients, rather than white individuals. Efforts to ensure equitable surgical outcomes need to focus on both patient-level, as well as community-specific factors.

Neighborhood-Level Social Disadvantage and Risk of Delirium Following Major Surgery

BACKGROUND/OBJECTIVES

Delirium is a common postoperative complication associated with prolonged length of stay, hospital readmission, and premature mortality. We explored the association between neighborhood-level characteristics and delirium incidence and severity, and compared neighborhood- with individual-level indicators of socioeconomic status in predicting delirium incidence.

DESIGN

A prospective observational cohort of patients enrolled between June 18, 2010, and August 8, 2013. Baseline interviews were conducted before surgery, and delirium/delirium severity was evaluated daily during hospitalization. Research staff evaluating delirium were blinded to baseline cognitive status.

SETTING

Two academic medical centers in Boston, MA.

PARTICIPANTS

A total of 560 older adults, aged 70 years or older, undergoing major noncardiac surgery.

INTERVENTION

The Area Deprivation Index (ADI) was used to characterize each neighborhood's socioeconomic disadvantage.

MEASUREMENTS

Delirium was assessed using the Confusion Assessment Method (CAM) long form. Delirium severity was calculated using the highest value of CAM Severity score (CAM-S) occurring during daily hospital assessments (CAM-S Peak).

RESULTS

Residing in the most disadvantaged neighborhoods (ADI > 44) was associated with a higher risk of incident delirium (12/26; 46%), compared with the least disadvantaged neighborhoods (122/534; 23%) (risk ratio (RR) (95% confidence interval (CI)) = 2.0 (1.3-3.1). The CAM-S Peak score was significantly associated with ADI (Spearman rank correlation, ρ = 0.11; P = .009). Mean CAM-S Peak scores generally rose from 3.7 to 5.3 across levels of increasing neighborhood disadvantage. The RR (95% CI) values associated with individual-level markers of socioeconomic status and cultural background were: 1.2 (0.9-1.7) for education of 12 years or less; 1.3 (0.8-2.1) for non-White race; and 1.7 (1.1-2.6) for annual household income of less than $20,000. None of these individual-level markers exceeded the ADI in terms of effect size or significance for prediction of delirium risk.

CONCLUSIONS

Neighborhood-level makers of social disadvantage are associated with delirium incidence and severity, and demonstrated an exposure-response relationship. Future studies should consider contextual-level metrics, such as the ADI, as risk markers of social disadvantage that can help to guide delirium treatment and prevention.

Defining critical factors in multi-country studies of assisted reproductive technologies (ART): data from the US and UK health systems

As the worldwide use of assisted reproductive technologies (ART) continues to grow, there is a critical need to assess the safety of these treatment parameters and the potential adverse health effects of their use in adults and their offspring. While key elements remain similar across nations, geographic variations both in treatments and populations make generalizability challenging. We describe and compare the demographic factors between the USA and the UK related to ART use and discuss implications for research. The USA and the UK share some common elements of ART practice and in how data are collected regarding long-term outcomes. However, the monitoring of ART in these two countries each brings strengths that complement each other's limitations.

Social Determinants of Pharmacy Deserts in Los Angeles County

As medications are commonly used to prevent and mitigate chronic diseases and their associated complications and outcomes, limited geographic access to medications in communities that are already plagued with health inequity is a growing concern. This is especially important because low-income urban minority communities often have high prevalence and incidence of cardiometabolic and respiratory chronic conditions. Community pharmacy deserts have been established in Chicago, New York, and other locales. In part because the definition was originally adapted from the concept of food deserts, existing studies have either utilized the distance of 1 mile or greater to the nearest community pharmacy solely, or used distance along with the same predefined social indicator thresholds that define food deserts (i.e., income and vehicle ownership), to define and identify areas as pharmacy deserts. No full analysis has been conducted of the social determinants that define and characterize medication shortage areas within a given locale, even though medication and food are usually accessed independently. Therefore, to address this gap in the literature, this study was designed to identify all potential “pharmacy deserts” in Los Angeles County based on distance alone and then characterize them by their social determinants of health (SDOH) indicators. Geographic pharmacy deserts were identified as census tracts where the nearest community pharmacy was 1 mile or more away from a tract centroid. K-means clustering was applied to group pharmacy deserts based on their composition of social determinants of health indicators. Twenty-five percent (571/2323) of LA County census tracts were pharmacy deserts and 75% (1752/2323) were pharmacy non-deserts. Within the desert areas, two statistically distinct groups of pharmacy deserts (type one and type two) emerged from the analysis. In comparison to type two pharmacy deserts, type one pharmacy deserts were characterized by a denser population, had more renters, more residents that speak English as a second language, less vehicle ownership, more residents living under the federal poverty level, more Black and Hispanic residents, more areas with higher crime against property and people, and less health professionals to serve the area. Residing in type one desert areas, potentially compounds the geographic shortage of pharmacies and pharmacy services. As such, residents in Los Angeles County pharmacy deserts might benefit greatly from equitable, innovative, community-based interventions that increase access to medications, pharmacy services, and pharmacists.

The social, economic, political, and genetic value of race and ethnicity in 2020

Disparities across racial and ethnic groups are present for a range of health outcomes. In this opinion piece, we consider the origin of racial and ethnic groupings, a history that highlights the sociopolitical nature of these terms. Indeed, the terms race and ethnicity exist purely as social constructs and must not be used interchangeably with genetic ancestry. There is no scientific evidence that the groups we traditionally call "races/ethnicities" have distinct, unifying biological or genetic basis. Such a focus runs the risk of compounding equity gaps and perpetuating erroneous conclusions. That said, we suggest that the terms race and ethnicity continue to have purpose as lenses through which to quantify and then close racial and ethnic disparities. Understanding the root cause of such health disparities-namely, longstanding racism and ethnocentrism-could promote interventions and policies poised to equitably improve population health.

Describing the density of high-level trauma centers in the 15 largest US cities

Background

There has been a proliferation of urban high-level trauma centers. The aim of this study was to describe the density of high-level adult trauma centers in the 15 largest cities in the USA and determine whether density was correlated with urban social determinants of health and violence rates.

Methods

The largest 15 US cities by population were identified. The American College of Surgeons' (ACS) and states' department of health websites were cross-referenced for designated high-level (levels 1 and 2) trauma centers in each city. Trauma centers and associated 20 min drive radius were mapped. High-level trauma centers per square mile and per population were calculated. The distance between high-level trauma centers was calculated. Publicly reported social determinants of health and violence data were tested for correlation with trauma center density.

Results

Among the 15 largest cities, 14 cities had multiple high-level adult trauma centers. There was a median of one high-level trauma center per every 150 square kilometers with a range of one center per every 39 square kilometers in Philadelphia to one center per596 square kilometers in San Antonio. There was a median of one high-level trauma center per 285 034 people with a range of one center per 175 058 people in Columbus to one center per 870 044 people in San Francisco. The median minimum distance between high-level trauma centers in the 14 cities with multiple centers was 8 kilometers and ranged from 1 kilometer in Houston to 43 kilometers in San Antonio. Social determinants of health, specifically poverty rate and unemployment rate, were highly correlated with violence rates. However, there was no correlation between trauma center density and social determinants of health or violence rates.

Discussion

High-level trauma centers density is not correlated with social determinants of health or violence rates.

Level of evidence

VI.

Study type

Economic/decision.

Variation in Risk of COVID-19 Infection and Predictors of Social Determinants of Health in Miami-Dade County, Florida

Miami–Dade County zip code–level (N = 91 zip codes) coronavirus disease 2019 (COVID-19) cases (N = 89,556 as of July 21, 2020) reported from the Florida Department of Health were used to estimate rates of COVID-19 per 1,000 population at the census block group level (N = 1,594 study block groups). To identify associations between rates of COVID-19 infections and multidimensional indexes of social determinants of health (SDOH) across Miami–Dade County, Florida, I applied a global model (ordinary least squares) and a local regression model (geographically weighted regression). Findings indicated that a social disadvantage index positively affected COVID-19 infection rates, whereas a socioeconomic status and opportunity index and a convergence of vulnerability index had an inverse but significant connection to COVID-19 infection rates over the study area. Rates of COVID-19 infections were localized to specific geographic areas and ranged from 0 to 60.75 per 1,000 population per square mile.

The intersection of genomics and big data with public health: Opportunities for precision public health

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.

Association of Neighborhood Characteristics with Utilization of High-Volume Hospitals Among Patients Undergoing High-Risk Cancer Surgery

INTRODUCTION

As high-risk cancer surgery continues to become more centralized, it is important to understand the association of neighborhood characteristics relative to access to surgical care. We sought to determine the neighborhood level characteristics that may be associated with travel patterns and utilization of high-volume hospitals.

METHODS

The California Office of Statewide Health Planning database was used to identify patients who underwent pancreatectomy (PD), esophagectomy (ES), proctectomy (PR), or pneumonectomy (PN) for cancer between 2014 and 2016. Total minutes (m) traveled as well as whether a patient bypassed the nearest hospital that performed the operation to get to a higher-volume center was assessed. Data were merged with the Centers for Disease control social vulnerability index (SVI).

RESULTS

Overall, 26,937 individuals (ES: 4.7%; PN: 53.5% PD: 13.9% PR: 27.9%) underwent a complex oncologic operation. Median travel time was 16 m (interquartile range [IQR] 8.3-30.24) [ES: 21.8 m (IQR 10.6-46.9); PN: 14 m (IQR 7.8-27.0); PD: 21.2 m (IQR 10.6-42.6); PR: 15 m (IQR 8.1-28.4)]. Nearly three-quarter of patients (ES: 34%; PN: 73%; PD: 72%; LR: 81%) underwent an operation at a high-volume hospital. For all four operations, patients who resided in a county with a high overall SVI were less likely to have surgery at a high-volume hospital (ES: odds ratio [OR] 0.39, 95% confidence interval [CI] 0.24-0.65; PN: OR: 0.67, 95% CI 0.51-0.88; PD: OR 0.61, 95% CI 0.44-0.84; PR: OR 0.76, 95% CI 0.58-0.98).

CONCLUSIONS

Patients residing in communities of high social vulnerability were less likely to undergo high-risk cancer surgery at a high-volume hospital. The identification of society-based contextual disparities in access to complex surgical care should serve to inform targeted strategies to direct additional resources toward these vulnerable communities.

Residential instability, neighborhood deprivation, and pediatric asthma outcomes

INTRODUCTION

Limited work has directly compared the role of different neighborhood factors or examined their interactive effects on pediatric asthma outcomes. Our objective was to quantify the main and interactive effects of neighborhood deprivation and residential instability (RI) on pediatric asthma outcomes.

METHODS

We conducted a retrospective cross-sectional study of patients with a primary diagnosis of asthma hospitalized at a tertiary care pediatric hospital. Residential addresses at the index hospitalization were linked to the state area deprivation index (ADI). RI was coded as the number of residences in the past 4 years. Logistic and ordinal regression and Cox regression survival analyses were used to estimate the effect on the primary outcomes of chronic asthma severity (intermittent, mild persistent, moderate persistent, severe persistent/other) as defined by the National Heart, Lung, and Blood Institute, severe hospitalization (requiring continuous albuterol or intensive care unit care), and time to emergency department (ED) readmission and rehospitalization within 365 days of the index visit, respectively.

RESULTS

In the sample (N = 664), 21% had severe persistent/other asthma, 22% had severe hospitalization, 37% were readmitted to the ED, and 19% were rehospitalized. Increasing RI was independently associated with more severe chronic asthma (odds ratio = 1.18, 95% confidence interval [CI] = 1.05, 1.32, P = .004), greater risk of 365-day ED readmission (hazard ratio [HR] = 1.10, 95% CI = 1.05, 1.15, P < .0001), and greater risk of 365-day rehospitalization (HR = 1.09, 95% CI = 1.03, 1.14, P = .002). There were no significant associations between ADI and these outcomes. Further, we did not find significant evidence of interactive effects.

CONCLUSIONS

RI appears to be modestly associated with pediatric asthma outcomes, independent of current neighborhood deprivation.

How Much Money Is Enough? Poverty and Health in Older People

OBJECTIVES

We examined the relationship between absolute income, adequacy of disposable income, and self-rated health among participants aged 60 years and over.

DESIGN

Cross sectional study.

SETTING

Community living older people in Hong Kong.

PARTICIPANTS

Older people aged 60 years and over in five districts in Hong Kong.

MEASUREMENTS

Data from a cross sectional survey of age friendly characteristics across five districts of Hong Kong carried out using stratified random sampling across a broad range of socioeconomic attributes.

RESULTS

Self-rated health showed a gradient for both absolute and adequacy of disposable income. The OR for the association between having just enough, or insufficient disposable income and poor health was higher: 2.0 and 3.6 respectively, and higher than that for absolute income (OR 1.8), and remained significant after adjustment for absolute income. No association between absolute income and self-rated health was observed among women. These findings suggest that adequacy of disposable income provide a stronger association with self-rated health compared with absolute income among older people aged 60 years and over, particularly for women. The absolute income corresponding to what is considered adequate disposable income lies between HK$4000-10000.

CONCLUSION

Adequacy of disposable income may be a better indicator than absolute income for older people in examining the relationship with health outcomes, particularly for older women.